
!apt update
!apt install -y --no-install-recommends \
        build-essential \
        curl \
        git \
        gnupg2 \
        make \
        cmake \
        ffmpeg \
        swig \
        libz-dev \
        unzip \
        zlib1g-dev \
        libglfw3 \
        libglfw3-dev \
        libxrandr2 \
        libxinerama-dev \
        libxi6 \
        libxcursor-dev \
        libgl1-mesa-dev \
        libgl1-mesa-glx \
        libglew-dev \
        libosmesa6-dev \
        lsb-release \
        ack-grep \
        patchelf \
        wget \
        xpra \
        xserver-xorg-dev \
        ffmpeg
!apt-get install python-opengl -y
!apt install xvfb -y


!pip install gymnasium[mujoco]
!pip install torch
!pip install tqdm
!pip install matplotlib
!pip install pyvirtualdisplay


!pip install otter-grader
!git clone https://github.com/chandar-lab/INF8250ae-assignments-2023.git public


#@title set up virtual display

from pyvirtualdisplay import Display

display = Display(visible=0, size=(1400, 900))
display.start()

<pyvirtualdisplay.display.Display at 0x79b023199300>


import gymnasium as gym
from gymnasium import wrappers
import torch
import numpy as np
from tqdm import tqdm
import matplotlib.pyplot as plt
import pickle
import os
import glob
import io
import base64
from IPython.display import HTML
from IPython import display as ipythondisplay


import otter
grader = otter.Notebook(colab=True, tests_dir='./public/a2/tests')


def plot(
    xs_list,
    means_list,
    stds_list,
    losses_list,
    labels_list=None,
    min=None,
    running_average=5,
):
    fig, ax = plt.subplots(1, 2, figsize=(10, 5))
    if labels_list is None:
        labels_list = [f"Agent {idx}" for idx in range(len(means_list))]
    for xs, means, stds, losses, label in zip(
        xs_list, means_list, stds_list, losses_list, labels_list
    ):
        kernel = np.ones(running_average) / running_average
        means_convolved = np.convolve(means, kernel, mode="same")
        stds_convolved = np.convolve(stds, kernel, mode="same")
        ax[0].plot(xs, means_convolved, label=label)
        ax[0].fill_between(
            xs,
            np.array(means_convolved) - np.array(stds_convolved),
            np.array(means_convolved) + np.array(stds_convolved),
            alpha=0.5,
        )
        ax[1].plot(xs, losses, label=label)
    if min is not None:
        ax[0].set_ylim(min, None)
    ax[0].legend()
    ax[0].set_ylabel("Reward")
    ax[1].set_ylabel("Loss")

    return fig, ax


class ExpertAgent(torch.nn.Module):
    def __init__(self, filename):
        super().__init__()
        self._network = torch.load(filename)
        self._network.eval()

    def get_action(self, obs: np.array):
        """
        Get action from the expert agent.

        Args:
            obs: np.array of shape (state_dim,)
        Returns:
            action: np.array of shape (action_dim,)
        """
        obs = torch.tensor(obs, dtype=torch.float32)
        return self._network(obs).cpu().detach().numpy()


def evaluate_agent(agent, env, num_episodes):
    """ Collect num_episodes trajectories for the agent and compute mean and std of the
    rewards. Remember to reset the environment before each episode.
    Args:
        agent: Agent, agent to evaluate
        env: gym.Env, environment to evaluate agent on
        num_episodes: int, number of episodes to evaluate the agent for
    Returns:
        mean_return: float, mean return over the episodes
        std_return: float, standard deviation of the return over the episodes
    """
    returns = []
    # TODO
    for episode in range(num_episodes):
      obs, _ = env.reset()
      ret = 0
      while (True):
        action = agent.get_action(obs)
        obs, reward, terminated, truncated, _ = env.step(action)
        ret += reward
        if terminated or truncated:
          break
      returns.append(ret)
    returns = np.array(returns)
    mean_returns = np.mean(returns)
    std_returns = np.std(returns)
    return float(mean_returns), float(std_returns)


grader.check("q1a")

q1a


VIDEO_LOCATION = "./content/video"


def show_video():
    mp4list = glob.glob(f"{VIDEO_LOCATION}/*.mp4")
    if len(mp4list) > 0:
        mp4 = mp4list[0]
        video = io.open(mp4, "r+b").read()
        encoded = base64.b64encode(video)
        ipythondisplay.display(
            HTML(
                data="""<video alt="test" autoplay
                loop controls style="height: 400px;">
                <source src="data:video/mp4;base64,{0}" type="video/mp4" />
             </video>""".format(
                    encoded.decode("ascii")
                )
            )
        )
    else:
        print("Could not find video")


def create_video(vis_env, agent, name_prefix="imitation_learning"):
    vis_env = wrappers.RecordVideo(vis_env, VIDEO_LOCATION, name_prefix=name_prefix)
    evaluate_agent(agent, vis_env, 1)
    vis_env.close_video_recorder()
    show_video()


env = gym.make("Ant-v4")
vis_env = gym.make("Ant-v4", render_mode="rgb_array")
a = env.action_space
expert_1mil = ExpertAgent("./public/a2/experts/network_1mil.pt")
mean, std = evaluate_agent(expert_1mil, env, 10)
print(f"Expert mean return: {mean} +/- {std}")
create_video(vis_env, expert_1mil, "expert_1mil")

Expert mean return: 4780.102965832734 +/- 45.003416180675174

/usr/local/lib/python3.10/dist-packages/gymnasium/wrappers/record_video.py:94: UserWarning: WARN: Overwriting existing videos at /content/content/video folder (try specifying a different `video_folder` for the `RecordVideo` wrapper if this is not desired)
  logger.warn(

Moviepy - Building video /content/content/video/expert_1mil-episode-0.mp4.
Moviepy - Writing video /content/content/video/expert_1mil-episode-0.mp4

Moviepy - Done !
Moviepy - video ready /content/content/video/expert_1mil-episode-0.mp4


class ReplayBuffer:
    def __init__(self, max_size=100_000):
        self._max_size = max_size
        self._states = None
        self._actions = None

    def add_rollouts(self, rollouts):
        """
        Add rollouts to the buffer

        Args:
            rollouts: dict, with keys "states" and "actions", with shapes
                (rollout_length, state_dim) and (rollout_length, action_dim)
                respectively.
        """
        if self._states is None:
            self._states = rollouts["states"][-self._max_size :]
            self._actions = rollouts["actions"][-self._max_size :]
        else:
            self._states = np.concatenate([self._states, rollouts["states"]])[
                -self._max_size :
            ]
            self._actions = np.concatenate([self._actions, rollouts["actions"]])[
                -self._max_size :
            ]

    def sample(self, batch_size):
        """
        Sample batch_size elements from the buffer without replacement.

        Args:
            batch_size: int, number of elements to sample
        Returns:
            states: np.array of shape (batch_size, state_dim)
            actions: np.array of shape (batch_size, action_dim)
        """
        if self._states is None or self._actions is None:
            raise ValueError("No data in buffer")

        # TODO: Sample batch_size random elements from self.states and self.actions
        rollout_size = len(self._states)
        rand_idx = np.random.choice(a = rollout_size, size=batch_size, replace = False)
        states = self._states[rand_idx]
        actions = self._actions[rand_idx]
        return states, actions

    def __len__(self):
        return len(self._states) if self._states is not None else 0


grader.check("q1b")

q1b


class Agent(torch.nn.Module):
    def __init__(self, obs_dim, action_dim):
        super().__init__()
        self._network = torch.nn.Sequential(
            torch.nn.Linear(obs_dim, 256),
            torch.nn.ReLU(),
            torch.nn.Linear(256, 256),
            torch.nn.ReLU(),
            torch.nn.Linear(256, action_dim),
        )

    def forward(self, obs_tensor: torch.Tensor):
        """
        Returns the actions for a batch of observations.

        Args:
            obs_tensor: torch.Tensor of shape (batch_size, obs_dim)
        Returns:
            action_tensor: torch.Tensor of shape (batch_size, action_dim)
        """
        # TODO
        return self._network(obs_tensor)

    def get_action(self, obs: np.ndarray) -> np.ndarray:
        """
        Get action from the agent for a single observation.

        Args:
            obs: np.ndarray of shape (obs_dim,)
        Returns:
            action: np.ndarray of shape (action_dim,)
        """

        # TODO Predict the action given the observation
        tensor_obs = torch.tensor(obs, dtype=torch.float32)
        return self._network(tensor_obs).cpu().detach().numpy()
        # pass


grader.check("q1c")

q1c


def behavior_cloning(agent, optimizer, buffer, batch_size=128, steps=1000):
    """
    Args:
        agent: Agent, agent to train
        optimizer: torch.optim.Optimizer, optimizer to use
        buffer: ReplayBuffer, buffer to sample from
        batch_size: int, batch size
        steps: int, number of steps to train
    Returns:
        loss: float, Average loss over the last 5 steps
    """

    losses = []
    # TODO: Implement the behavior cloning training loop
    # Hint: Store the loss values in losses list to compute the final average over the
    # last 5 steps
    # Hint: Take a look at torch.nn.functional for useful functions for computing the
    # loss

    for step in range(steps):
      states, actions = buffer.sample(batch_size)

      states = torch.tensor(states, dtype=torch.float32)
      actions = torch.tensor(actions, dtype=torch.float32)
      preds = agent(states)

      loss = torch.nn.functional.mse_loss(input=preds, target=actions, reduction='mean')

      optimizer.zero_grad()
      loss.backward()
      optimizer.step()

      losses.append(loss.cpu().detach().numpy())

    return float(np.mean(losses[-5:]))


grader.check("q2.a")

q2.a


with open("./public/a2/expert_data/expert_data_Ant-v4.pkl", "rb") as f:
    data = pickle.load(f)
    states = np.concatenate([trajectory["observation"][:, :27] for trajectory in data])
    actions = np.concatenate([trajectory["action"] for trajectory in data])
    data_average_reward = np.mean([np.sum(trajectory["reward"]) for trajectory in data])
print(f"Average expert return: {data_average_reward}")

Average expert return: 4713.6533203125


BATCH_SIZE = 128
STEPS = 1000
bc_agent = Agent(env.observation_space.shape[0], env.action_space.shape[0])
optimizer = torch.optim.Adam(bc_agent.parameters(), lr=5e-3)
bc_buffer = ReplayBuffer()

# TODO: Add the states and actions from the expert curated data to the buffer.
# Then run behavior cloning and evaluate the agent.

bc_buffer.add_rollouts(rollouts={'states':states, 'actions':actions})
behavior_cloning(bc_agent, optimizer, bc_buffer, batch_size=BATCH_SIZE, steps=STEPS)

mean, std = evaluate_agent(bc_agent, env, num_episodes=10)


print(
    f"The agent trained on the curated dataset has an average reward of {mean} +/- {std}"
)
create_video(vis_env, bc_agent, name_prefix="ant_curated")

The agent trained on the curated dataset has an average reward of 4389.037708954816 +/- 106.82948103202956

/usr/local/lib/python3.10/dist-packages/gymnasium/wrappers/record_video.py:94: UserWarning: WARN: Overwriting existing videos at /content/content/video folder (try specifying a different `video_folder` for the `RecordVideo` wrapper if this is not desired)
  logger.warn(

Moviepy - Building video /content/content/video/ant_curated-episode-0.mp4.
Moviepy - Writing video /content/content/video/ant_curated-episode-0.mp4

Moviepy - Done !
Moviepy - video ready /content/content/video/ant_curated-episode-0.mp4


def relabel_with_expert(states, expert_agent):
    """
    Args:
        states: np.array of shape (batch_size, state_dim)
        expert_agent: ExpertAgent
    Returns:
        actions: np.array of shape (batch_size, action_dim)
    """
    actions = []

    # TODO: Loop through the states, and get the expert action
    # for each state
    # Hint: Use expert_agent.get_action
    actions = expert_agent.get_action(states)
    return np.array(actions)


def collect_rollouts(env, agent, n_to_collect=1000):
    """
    Args:
        env: gym.Env
        agent: Agent
        n_to_collect: int, number of states to collect
    Returns:
        states: np.array of shape (n_to_collect, state_dim)
        actions: np.array of shape (n_to_collect, action_dim)
    """
    states = []
    actions = []
    state, _ = env.reset()
    done = False

    ### TODO: Collect rollouts until we have n_to_collect states
    # Hint: Remember to reset the environment when a rollout is finished

    for _ in range(n_to_collect):
      old_state = state
      action = agent.get_action(old_state)
      state, reward, terminated, truncated, _ = env.step(action)

      states.append(old_state)
      actions.append(action)

      done = terminated or truncated
      if done:
        state, _ = env.reset()
        continue
    env.reset()
    return np.array(states), np.array(actions)


def seed_data(env, expert_agent, buffer, n_to_collect=1000):
    """
    Collects rollouts using the expert agent and adds them to the buffer.

    Args:
        env: gym.Env
        expert_agent: ExpertAgent
        buffer: ReplayBuffer
        n_to_collect: int, number of samples to collect
    """

    ### TODO: Implement this function
    states, actions = collect_rollouts(env, expert_agent, n_to_collect)
    buffer.add_rollouts({'states':states, 'actions':actions})


grader.check("q3")

q3


def dagger_iteration(
    agent,
    optimizer,
    expert_agent,
    env,
    buffer,
    n_to_collect,
    steps=1000,
    batch_size=128,
):
    """
    Implements one iteration of the DAgger algorithm. Collects the rollouts using the
    agent, relabels them using the expert, and trains the agent for `steps` steps using
    behavior cloning.

    Args:
        agent: Agent
        optimizer: torch.optim.Optimizer
        expert_agent: ExpertAgent
        env: gym.Env
        buffer: ReplayBuffer
        n_to_collect: int, number of samples to collect
        steps: int, number of steps to train
        batch_size: int, batch size
    Returns:
        loss: float, Average loss over the last 5 steps of behavior
            cloning
    """

    ### TODO: Implement one iteration of the DAgger algorithm
    states, actions = collect_rollouts(env, agent, n_to_collect)
    expert_actions = relabel_with_expert(states, expert_agent)
    buffer.add_rollouts({'states':states, 'actions':expert_actions})
    loss = behavior_cloning(agent, optimizer, buffer, batch_size=batch_size, steps=steps)

    return loss


def dagger(
    agent,
    optimizer,
    expert_agent,
    env,
    buffer,
    collect_per_iteration=2000,
    n_iterations=10,
    gradient_steps=1000,
    batch_size=128,
    n_episodes_eval=10,
):
    """
    Runs the DAgger algorithm for `n_iterations` iterations. The loss from each
    iteration is stored and returned. After each iteration, the agent is evaluated for
    `n_episodes_eval` episodes. The mean and std of the rewards are stored and returned.

    Args:
        agent: Agent
        optimizer: torch.optim.Optimizer
        expert_agent: ExpertAgent
        env: gym.Env
        buffer: ReplayBuffer
        collect_per_iteration: int, number of samples to collect per iteration
        n_iterations: int, number of DAgger iterations
        gradient_steps: int, number of steps to train the agent for per iteration
        batch_size: int, batch size
        n_episodes_eval: int, number of episodes to evaluate the agent for
    Returns:
        losses: list of floats, losses from each DAgger iteration
        means: list of floats, mean rewards from each DAgger iteration
        stds: list of floats, std of rewards from each DAgger iteration
    """
    losses, means, stds = [], [], []

    ### TODO: Implement the DAgger algorithm
    # Hint: It might be helpful when running stuff later on to also print
    # which iteration of DAgger you are on

    for _ in range(n_iterations):
      iteration_loss = dagger_iteration(agent, optimizer, expert_agent, env, buffer
                                     , collect_per_iteration, gradient_steps,
                                     batch_size)
      losses.append(iteration_loss)

      mean, std = evaluate_agent(agent, env, n_episodes_eval)
      means.append(mean)
      stds.append(std)

      # print(f'Daggar iter {iteration}: loss {iteration_loss} mean rewards {mean}, std reward {std}')

    return losses, means, stds


# Run DAgger starting from agent pretrained on curated data data
expert = ExpertAgent("./public/a2/experts/network_1mil.pt")
agent = bc_agent
buffer = bc_buffer
optimizer = torch.optim.Adam(agent.parameters(), lr=5e-3)
losses_bc, means_bc, stds_bc = dagger(
    agent, optimizer, expert, env, buffer, 2000, 15, 2000, 128, 10
)

# Run DAgger starting from scratch, using the same expert
agent = Agent(env.observation_space.shape[0], env.action_space.shape[0])
buffer = ReplayBuffer()
optimizer = torch.optim.Adam(agent.parameters(), lr=5e-3)
seed_data(env, expert, buffer, 2000)
losses_scratch, means_scratch, stds_scratch = dagger(
    agent, optimizer, expert, env, buffer, 2000, 15, 2000, 128, 10
)
plot(
    [np.arange(len(losses_bc)), np.arange(len(losses_scratch))],
    [means_bc, means_scratch],
    [stds_bc, stds_scratch],
    [losses_bc, losses_scratch],
    ["BC", "Scratch"],
    running_average=1,
)

(<Figure size 1000x500 with 2 Axes>,
 array([<Axes: ylabel='Reward'>, <Axes: ylabel='Loss'>], dtype=object))


expert_2mil = ExpertAgent("./public/a2/experts/network_2mil.pt")
mean, std = evaluate_agent(expert_2mil, env, 10)
print(f"Expert mean return: {mean} +/- {std}")
create_video(vis_env, expert_2mil, "expert_2mil")

Expert mean return: 5842.196336294514 +/- 89.33619663523491
Moviepy - Building video /content/content/video/expert_2mil-episode-0.mp4.
Moviepy - Writing video /content/content/video/expert_2mil-episode-0.mp4

Moviepy - Done !
Moviepy - video ready /content/content/video/expert_2mil-episode-0.mp4


N_ITERS = 50
N_DATA_PER_ITER = 2000
N_GRADIENT_STEPS = 2000
expert_strength_data = {
    "all_means": [],
    "all_stds": [],
    "all_losses": [],
    "all_xs": [],
}
for expert in [expert_1mil, expert_2mil]:
    agent = Agent(env.observation_space.shape[0], env.action_space.shape[0])
    optimizer = torch.optim.Adam(agent.parameters(), lr=5e-3)
    buffer = ReplayBuffer()
    seed_data(env, expert, buffer, 2000)

    # TODO: Run DAgger for the given expert
    losses, means, stds = dagger(agent, optimizer, expert, env, buffer, N_DATA_PER_ITER, N_ITERS, N_GRADIENT_STEPS, batch_size=128, n_episodes_eval=10)

    xs = np.arange(N_ITERS) + 1
    expert_strength_data["all_xs"].append(xs)
    expert_strength_data["all_means"].append(means)
    expert_strength_data["all_stds"].append(stds)
    expert_strength_data["all_losses"].append(losses)


plot(
    expert_strength_data["all_xs"],
    expert_strength_data["all_means"],
    expert_strength_data["all_stds"],
    expert_strength_data["all_losses"],
    [f"{expert} expert" for expert in ["1mil", "2mil"]],
    min=-1000,
)

(<Figure size 1000x500 with 2 Axes>,
 array([<Axes: ylabel='Reward'>, <Axes: ylabel='Loss'>], dtype=object))


TOTAL_DATA = 100_000
TOTAL_GRADIENT_STEPS = 100_000
n_iters_data = {
    "all_means": [],
    "all_stds": [],
    "all_losses": [],
    "all_xs": [],
}
expert = ExpertAgent("./public/a2/experts/network_2mil.pt")
for n_iters in [5, 25, 50, 100, 200]:
    agent = Agent(env.observation_space.shape[0], env.action_space.shape[0])
    optimizer = torch.optim.Adam(agent.parameters(), lr=5e-3)
    buffer = ReplayBuffer()
    seed_data(env, expert, buffer, 2000)

    # TODO: Run DAgger for n_iters iterations
    grad_steps = int(TOTAL_GRADIENT_STEPS / n_iters)
    n_data_per_iter = int(TOTAL_DATA / n_iters)

    losses, means, stds = dagger(agent, optimizer, expert, env, buffer, n_data_per_iter, n_iters, grad_steps, batch_size=128, n_episodes_eval=10)

    xs = 100_000 / n_iters * (np.arange(n_iters) + 1)
    n_iters_data["all_xs"].append(xs)
    n_iters_data["all_means"].append(means)
    n_iters_data["all_stds"].append(stds)
    n_iters_data["all_losses"].append(losses)


plot(
    n_iters_data["all_xs"],
    n_iters_data["all_means"],
    n_iters_data["all_stds"],
    n_iters_data["all_losses"],
    [f"{n_iters} iters" for n_iters in [5, 25, 50, 100, 200]],
    min=-1000,
)

(<Figure size 1000x500 with 2 Axes>,
 array([<Axes: ylabel='Reward'>, <Axes: ylabel='Loss'>], dtype=object))

Assignment 2: Imitation Learning¶

Instructions¶

Q1 Getting started with RL (10 pts)¶

Q1.a: Agent Evaluation (4 pts)¶

Q1.b: Replay Buffer (3 pts)¶

Q1.c: Agent (3 pts)¶

Q2: Behavior cloning (20 pts)¶

Q2.a Implement Behavior Cloning (15 pts)¶

Q2.b Run Behavior Cloning (5 pts)¶

Q3 DAgger Implementation (30 pts)¶

Q4 Analyzing DAgger¶

Q4.a: DAgger with policy drift¶

Q4.b Exploring the effect of the effect of the strength of the expert on DAgger¶

Q4.c Exploring the effect of the number of iterations on DAgger¶